AnishJoshi
/

codellama2-finetuned-nl2bash-fin

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
 ---
 # Model Card for Model ID
+Finetuned on the [AnishJoshi/nl2bash-custom](https://huggingface.co/datasets/AnishJoshi/nl2bash-custom) dataset for generating bash code based on natural language descriptions.
 ## Model Details
+- **Model Name:** CodeLlama2-Finetuned-NL2Bash
+- **Base Model:** CodeLlama2
+- **Task:** Natural Language to Bash Script Conversion
+- **Framework:** PyTorch
+- **Fine-tuning Dataset:** Custom dataset of natural language commands and corresponding Bash scripts, available [here](https://huggingface.co/datasets/AnishJoshi/nl2bash-custom)
+### Model Description
+- **Developed by:** Anish Joshi
+- **Model type:** CausalLM
+- **Finetuned from model:** Codellama2
+## Files Included
+- `adapter_config.json`: Configuration file for the adapter layers.
+- `adapter_model.safetensors`: Weights of the adapter layers in the Safetensors format.
+- `optimizer.pt`: State of the optimizer used during training.
+- `rng_state.pth`: State of the random number generator.
+- `scheduler.pt`: State of the learning rate scheduler.
+- `special_tokens_map.json`: Mapping for special tokens used by the tokenizer.
+- `tokenizer.json`: Tokenizer model including the vocabulary.
+- `tokenizer_config.json`: Configuration settings for the tokenizer.
+- `trainer_state.json`: State of the trainer including training metrics.
+- `training_args.bin`: Training arguments used for fine-tuning.
+- `README.md
+## Usage
+# Load model directly
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")
+model = AutoModelForCausalLM.from_pretrained("AnishJoshi/codellama2-finetuned-nl2bash-fin")
+```
 ## Training Details
+Training details available at [Finetuning Notebook](https://github.com/AnishJoshi13/Bash-Scripting-Code-Assistant/tree/main/notebooks/finetuning.ipynb)
 #### Training Hyperparameters
+Training arguments and configuration are set using TrainingArguments and LoraConfig. The model is fine-tuned using the following parameters:
+- `output_dir: codellama2-finetuned-nl2bash` - Directory to save the fine-tuned model.
+- `per_device_train_batch_size`: 2 - Batch size per device.
+- `gradient_accumulation_steps`: 16 - Number of gradient accumulation steps.
+- `optim`: paged_adamw_32bit - Optimizer type.
+- `learning_rate`: 2e-4 - Learning rate.
+- `lr_scheduler_type`: cosine - Learning rate scheduler type.
+- `save_strategy`: epoch - Strategy to save checkpoints.
+- `logging_steps`: 10 - Number of steps between logging.
+- `num_train_epochs`: 1 - Number of training epochs.
+- `max_steps`: 100 - Maximum number of training steps.
+- `fp16`: True - Use 16-bit floating-point precision.
+- `push_to_hub`: False - Whether to push the model to Hugging Face Hub.
+- `report_to`: none - Reporting destination.
 ## Evaluation
+Evaulation metrics and calculations available at [Evaluation Notebook](https://github.com/AnishJoshi13/Bash-Scripting-Code-Assistant/tree/main/notebooks/evaluation.ipynb)